Simultaneous recognition of words and prosody in the Boston University Radio Speech Corpus

This paper describes automatic speech recognition systems that satisfy two technological objectives. First, we seek to improve the automatic labeling of prosody, in order to aid future research in automatic speech understanding. Second, we seek to apply statistical speech recognition models of prosody for the purpose of reducing the word error rate of an automatic speech recognizer. The systems described in this paper are variants of a core dynamic Bayesian network model, in which the key hidden variables are the word, the prosodic tag sequence, and the prosody-dependent allophones. Statistical models of the interaction among words and prosodic tags are trained using the Boston University Radio Speech Corpus, a database annotated using the tones and break indices (ToBI) prosodic annotation system. This paper presents both theoretical and empirical results in support of the conclusion that a prosody-dependent speech recognizer—a recognizer that simultaneously computes the most-probable word labels and prosodic tags—can provide lower word recognition error rates than a standard prosody-independent speech recognizer in a multi-speaker speaker-dependent speech recognition task on radio speech.